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Found 7 papers, 2 papers with code
Do Transformer World Models Give Better Policy Gradients?
We integrate such AWMs into a policy gradient framework that underscores the relationship between network architectures and the policy gradient updates they inherently represent.
Bridging State and History Representations: Understanding Self-Predictive RL
These findings culminate in a set of preliminary guidelines for RL practitioners.
Course Correcting Koopman Representations
Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space.
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization
Estimating the per-state expected cumulative rewards is a critical aspect of reinforcement learning approaches, however the experience is obtained, but standard deep neural-network function-approximation methods are often inefficient in this setting.
Model-based Reinforcement Learning
reinforcement-learning
+1
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators
In this paper, we propose to leverage domain-independent heuristic functions commonly used in the classical planning literature to improve the sample efficiency of RL.
Batched Large-scale Bayesian Optimization in High-dimensional Spaces
Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries.
Incremental Truncated LSTD
Balancing between computational efficiency and sample efficiency is an important goal in reinforcement learning.
